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Cloud computing platform for real-time measurement and verification of energy performance

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  • Ke, Ming-Tsun
  • Yeh, Chia-Hung
  • Su, Cheng-Jie

Abstract

Nations worldwide are vigorously promoting policies to improve energy efficiency. The use of measurement and verification (M&V) procedures to quantify energy performance is an essential topic in this field. Currently, energy performance M&V is accomplished via a combination of short-term on-site measurements and engineering calculations. This requires extensive amounts of time and labor and can result in a discrepancy between actual energy savings and calculated results. In addition, the M&V period typically lasts for periods as long as several months or up to a year, the failure to immediately detect abnormal energy performance not only decreases energy performance, results in the inability to make timely correction, and misses the best opportunity to adjust or repair equipment and systems.

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  • Ke, Ming-Tsun & Yeh, Chia-Hung & Su, Cheng-Jie, 2017. "Cloud computing platform for real-time measurement and verification of energy performance," Applied Energy, Elsevier, vol. 188(C), pages 497-507.
    • Handle: RePEc:eee:appene:v:188:y:2017:i:c:p:497-507
    DOI: 10.1016/j.apenergy.2016.12.034
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    Cited by:

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    2. Perroni, Marcos G. & Gouvea da Costa, Sergio E. & Pinheiro de Lima, Edson & Vieira da Silva, Wesley & Tortato, Ubiratã, 2018. "Measuring energy performance: A process based approach," Applied Energy, Elsevier, vol. 222(C), pages 540-553.
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    5. Díaz, Julián Arco & Ramos, José Sánchez & Delgado, M. Carmen Guerrero & García, David Hidalgo & Montoya, Francisco Gil & Domínguez, Servando Álvarez, 2018. "A daily baseline model based on transfer functions for the verification of energy saving. A case study of the administration room at the Palacio de la Madraza, Granada," Applied Energy, Elsevier, vol. 224(C), pages 538-549.
    6. Severinsen, A. & Myrland, Ø., 2022. "ShinyRBase: Near real-time energy saving models using reactive programming," Applied Energy, Elsevier, vol. 325(C).
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    8. Marco Pau & Panagiotis Kapsalis & Zhiyu Pan & George Korbakis & Dario Pellegrino & Antonello Monti, 2022. "MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain," Energies, MDPI, vol. 15(7), pages 1-22, April.

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